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added Generalized Hough implementation

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This commit is contained in:
Vladislav Vinogradov
2012-09-10 16:24:55 +04:00
parent 86c7e183d2
commit 98c92f196e
10 changed files with 4037 additions and 154 deletions
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36
modules/gpu/include/opencv2/gpu/gpu.hpp
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@@ -770,11 +770,11 @@ CV_EXPORTS void blendLinear(const GpuMat& img1, const GpuMat& img2, const GpuMat
GpuMat& result, Stream& stream = Stream::Null());
//! Performa bilateral filtering of passsed image
CV_EXPORTS void bilateralFilter(const GpuMat& src, GpuMat& dst, int kernel_size, float sigma_color, float sigma_spatial,
CV_EXPORTS void bilateralFilter(const GpuMat& src, GpuMat& dst, int kernel_size, float sigma_color, float sigma_spatial,
int borderMode = BORDER_DEFAULT, Stream& stream = Stream::Null());
//! Brute force non-local means algorith (slow but universal)
CV_EXPORTS void nonLocalMeans(const GpuMat& src, GpuMat& dst, float h,
CV_EXPORTS void nonLocalMeans(const GpuMat& src, GpuMat& dst, float h,
int search_widow_size = 11, int block_size = 7, int borderMode = BORDER_DEFAULT, Stream& s = Stream::Null());
@@ -854,6 +854,38 @@ CV_EXPORTS void HoughCircles(const GpuMat& src, GpuMat& circles, int method, flo
CV_EXPORTS void HoughCircles(const GpuMat& src, GpuMat& circles, HoughCirclesBuf& buf, int method, float dp, float minDist, int cannyThreshold, int votesThreshold, int minRadius, int maxRadius, int maxCircles = 4096);
CV_EXPORTS void HoughCirclesDownload(const GpuMat& d_circles, OutputArray h_circles);
//! finds arbitrary template in the grayscale image using Generalized Hough Transform
//! Ballard, D.H. (1981). Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition 13 (2): 111-122.
//! Guil, N., González-Linares, J.M. and Zapata, E.L. (1999). Bidimensional shape detection using an invariant approach. Pattern Recognition 32 (6): 1025-1038.
class CV_EXPORTS GeneralizedHough_GPU : public Algorithm
{
public:
static Ptr<GeneralizedHough_GPU> create(int method);
virtual ~GeneralizedHough_GPU();
//! set template to search
void setTemplate(const GpuMat& templ, int cannyThreshold = 100, Point templCenter = Point(-1, -1));
void setTemplate(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy, Point templCenter = Point(-1, -1));
//! find template on image
void detect(const GpuMat& image, GpuMat& positions, int cannyThreshold = 100);
void detect(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy, GpuMat& positions);
void download(const GpuMat& d_positions, OutputArray h_positions, OutputArray h_votes = noArray());
void release();
protected:
virtual void setTemplateImpl(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy, Point templCenter) = 0;
virtual void detectImpl(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy, GpuMat& positions) = 0;
virtual void releaseImpl() = 0;
private:
GpuMat edges_;
CannyBuf cannyBuf_;
};
////////////////////////////// Matrix reductions //////////////////////////////
//! computes mean value and standard deviation of all or selected array elements

94
modules/gpu/perf/perf_imgproc.cpp
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@@ -1713,4 +1713,98 @@ PERF_TEST_P(Sz_Dp_MinDist, ImgProc_HoughCircles, Combine(GPU_TYPICAL_MAT_SIZES,
}
}
//////////////////////////////////////////////////////////////////////
// GeneralizedHough
CV_FLAGS(GHMethod, cv::GHT_POSITION, cv::GHT_SCALE, cv::GHT_ROTATION);
DEF_PARAM_TEST(Method_Sz, GHMethod, cv::Size);
PERF_TEST_P(Method_Sz, GeneralizedHough, Combine(
Values(GHMethod(cv::GHT_POSITION), GHMethod(cv::GHT_POSITION | cv::GHT_SCALE), GHMethod(cv::GHT_POSITION | cv::GHT_ROTATION), GHMethod(cv::GHT_POSITION | cv::GHT_SCALE | cv::GHT_ROTATION)),
GPU_TYPICAL_MAT_SIZES))
{
declare.time(10);
const int method = GET_PARAM(0);
const cv::Size imageSize = GET_PARAM(1);
const cv::Mat templ = readImage("cv/shared/templ.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(templ.empty());
cv::Mat image(imageSize, CV_8UC1, cv::Scalar::all(0));
cv::RNG rng(123456789);
const int objCount = rng.uniform(5, 15);
for (int i = 0; i < objCount; ++i)
{
double scale = rng.uniform(0.7, 1.3);
bool rotate = rng.uniform(0, 2);
cv::Mat obj;
cv::resize(templ, obj, cv::Size(), scale, scale);
if (rotate)
obj = obj.t();
cv::Point pos;
pos.x = rng.uniform(0, image.cols - obj.cols);
pos.y = rng.uniform(0, image.rows - obj.rows);
cv::Mat roi = image(cv::Rect(pos, obj.size()));
cv::add(roi, obj, roi);
}
cv::Mat edges;
cv::Canny(image, edges, 50, 100);
cv::Mat dx, dy;
cv::Sobel(image, dx, CV_32F, 1, 0);
cv::Sobel(image, dy, CV_32F, 0, 1);
if (runOnGpu)
{
cv::gpu::GpuMat d_edges(edges);
cv::gpu::GpuMat d_dx(dx);
cv::gpu::GpuMat d_dy(dy);
cv::gpu::GpuMat d_position;
cv::Ptr<cv::gpu::GeneralizedHough_GPU> d_hough = cv::gpu::GeneralizedHough_GPU::create(method);
if (method & cv::GHT_ROTATION)
{
d_hough->set("maxAngle", 90.0);
d_hough->set("angleStep", 2.0);
}
d_hough->setTemplate(cv::gpu::GpuMat(templ));
d_hough->detect(d_edges, d_dx, d_dy, d_position);
TEST_CYCLE()
{
d_hough->detect(d_edges, d_dx, d_dy, d_position);
}
}
else
{
cv::Mat positions;
cv::Ptr<cv::GeneralizedHough> hough = cv::GeneralizedHough::create(method);
if (method & cv::GHT_ROTATION)
{
hough->set("maxAngle", 90.0);
hough->set("angleStep", 2.0);
}
hough->setTemplate(templ);
hough->detect(edges, dx, dy, positions);
TEST_CYCLE()
{
hough->detect(edges, dx, dy, positions);
}
}
}
} // namespace

1034
modules/gpu/src/cuda/hough.cu
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1097
modules/gpu/src/hough.cpp
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256
modules/gpu/test/test_hough.cpp Normal file
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@@ -0,0 +1,256 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp"
#ifdef HAVE_CUDA
namespace {
///////////////////////////////////////////////////////////////////////////////////////////////////////
// HoughLines
PARAM_TEST_CASE(HoughLines, cv::gpu::DeviceInfo, cv::Size, UseRoi)
{
static void generateLines(cv::Mat& img)
{
img.setTo(cv::Scalar::all(0));
cv::line(img, cv::Point(20, 0), cv::Point(20, img.rows), cv::Scalar::all(255));
cv::line(img, cv::Point(0, 50), cv::Point(img.cols, 50), cv::Scalar::all(255));
cv::line(img, cv::Point(0, 0), cv::Point(img.cols, img.rows), cv::Scalar::all(255));
cv::line(img, cv::Point(img.cols, 0), cv::Point(0, img.rows), cv::Scalar::all(255));
}
static void drawLines(cv::Mat& dst, const std::vector<cv::Vec2f>& lines)
{
dst.setTo(cv::Scalar::all(0));
for (size_t i = 0; i < lines.size(); ++i)
{
float rho = lines[i][0], theta = lines[i][1];
cv::Point pt1, pt2;
double a = std::cos(theta), b = std::sin(theta);
double x0 = a*rho, y0 = b*rho;
pt1.x = cvRound(x0 + 1000*(-b));
pt1.y = cvRound(y0 + 1000*(a));
pt2.x = cvRound(x0 - 1000*(-b));
pt2.y = cvRound(y0 - 1000*(a));
cv::line(dst, pt1, pt2, cv::Scalar::all(255));
}
}
};
TEST_P(HoughLines, Accuracy)
{
const cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
const cv::Size size = GET_PARAM(1);
const bool useRoi = GET_PARAM(2);
const float rho = 1.0f;
const float theta = 1.5f * CV_PI / 180.0f;
const int threshold = 100;
cv::Mat src(size, CV_8UC1);
generateLines(src);
cv::gpu::GpuMat d_lines;
cv::gpu::HoughLines(loadMat(src, useRoi), d_lines, rho, theta, threshold);
std::vector<cv::Vec2f> lines;
cv::gpu::HoughLinesDownload(d_lines, lines);
cv::Mat dst(size, CV_8UC1);
drawLines(dst, lines);
ASSERT_MAT_NEAR(src, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, HoughLines, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
WHOLE_SUBMAT));
///////////////////////////////////////////////////////////////////////////////////////////////////////
// HoughCircles
PARAM_TEST_CASE(HoughCircles, cv::gpu::DeviceInfo, cv::Size, UseRoi)
{
static void drawCircles(cv::Mat& dst, const std::vector<cv::Vec3f>& circles, bool fill)
{
dst.setTo(cv::Scalar::all(0));
for (size_t i = 0; i < circles.size(); ++i)
cv::circle(dst, cv::Point2f(circles[i][0], circles[i][1]), (int)circles[i][2], cv::Scalar::all(255), fill ? -1 : 1);
}
};
TEST_P(HoughCircles, Accuracy)
{
const cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
const cv::Size size = GET_PARAM(1);
const bool useRoi = GET_PARAM(2);
const float dp = 2.0f;
const float minDist = 10.0f;
const int minRadius = 10;
const int maxRadius = 20;
const int cannyThreshold = 100;
const int votesThreshold = 20;
std::vector<cv::Vec3f> circles_gold(4);
circles_gold[0] = cv::Vec3i(20, 20, minRadius);
circles_gold[1] = cv::Vec3i(90, 87, minRadius + 3);
circles_gold[2] = cv::Vec3i(30, 70, minRadius + 8);
circles_gold[3] = cv::Vec3i(80, 10, maxRadius);
cv::Mat src(size, CV_8UC1);
drawCircles(src, circles_gold, true);
cv::gpu::GpuMat d_circles;
cv::gpu::HoughCircles(loadMat(src, useRoi), d_circles, CV_HOUGH_GRADIENT, dp, minDist, cannyThreshold, votesThreshold, minRadius, maxRadius);
std::vector<cv::Vec3f> circles;
cv::gpu::HoughCirclesDownload(d_circles, circles);
ASSERT_FALSE(circles.empty());
for (size_t i = 0; i < circles.size(); ++i)
{
cv::Vec3f cur = circles[i];
bool found = false;
for (size_t j = 0; j < circles_gold.size(); ++j)
{
cv::Vec3f gold = circles_gold[j];
if (std::fabs(cur[0] - gold[0]) < minDist && std::fabs(cur[1] - gold[1]) < minDist && std::fabs(cur[2] - gold[2]) < minDist)
{
found = true;
break;
}
}
ASSERT_TRUE(found);
}
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, HoughCircles, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
WHOLE_SUBMAT));
///////////////////////////////////////////////////////////////////////////////////////////////////////
// GeneralizedHough
PARAM_TEST_CASE(GeneralizedHough, cv::gpu::DeviceInfo, UseRoi)
{
};
TEST_P(GeneralizedHough, POSITION)
{
const cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
const bool useRoi = GET_PARAM(1);
cv::Mat templ = readImage("../cv/shared/templ.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(templ.empty());
cv::Point templCenter(templ.cols / 2, templ.rows / 2);
const size_t gold_count = 3;
cv::Point pos_gold[gold_count];
pos_gold[0] = cv::Point(templCenter.x + 10, templCenter.y + 10);
pos_gold[1] = cv::Point(2 * templCenter.x + 40, templCenter.y + 10);
pos_gold[2] = cv::Point(2 * templCenter.x + 40, 2 * templCenter.y + 40);
cv::Mat image(templ.rows * 3, templ.cols * 3, CV_8UC1, cv::Scalar::all(0));
for (size_t i = 0; i < gold_count; ++i)
{
cv::Rect rec(pos_gold[i].x - templCenter.x, pos_gold[i].y - templCenter.y, templ.cols, templ.rows);
cv::Mat imageROI = image(rec);
templ.copyTo(imageROI);
}
cv::Ptr<cv::gpu::GeneralizedHough_GPU> hough = cv::gpu::GeneralizedHough_GPU::create(cv::GHT_POSITION);
hough->set("votesThreshold", 200);
hough->setTemplate(loadMat(templ, useRoi));
cv::gpu::GpuMat d_pos;
hough->detect(loadMat(image, useRoi), d_pos);
std::vector<cv::Vec4f> pos;
hough->download(d_pos, pos);
ASSERT_EQ(gold_count, pos.size());
for (size_t i = 0; i < gold_count; ++i)
{
cv::Point gold = pos_gold[i];
bool found = false;
for (size_t j = 0; j < pos.size(); ++j)
{
cv::Point2f p(pos[j][0], pos[j][1]);
if (::fabs(p.x - gold.x) < 2 && ::fabs(p.y - gold.y) < 2)
{
found = true;
break;
}
}
ASSERT_TRUE(found);
}
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, GeneralizedHough, testing::Combine(
ALL_DEVICES,
WHOLE_SUBMAT));
} // namespace
#endif // HAVE_CUDA

136
modules/gpu/test/test_imgproc.cpp
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@@ -1126,142 +1126,6 @@ INSTANTIATE_TEST_CASE_P(GPU_ImgProc, CornerMinEigen, testing::Combine(
testing::Values(BlockSize(3), BlockSize(5), BlockSize(7)),
testing::Values(ApertureSize(0), ApertureSize(3), ApertureSize(5), ApertureSize(7))));
///////////////////////////////////////////////////////////////////////////////////////////////////////
// HoughLines
PARAM_TEST_CASE(HoughLines, cv::gpu::DeviceInfo, cv::Size, UseRoi)
{
static void generateLines(cv::Mat& img)
{
img.setTo(cv::Scalar::all(0));
cv::line(img, cv::Point(20, 0), cv::Point(20, img.rows), cv::Scalar::all(255));
cv::line(img, cv::Point(0, 50), cv::Point(img.cols, 50), cv::Scalar::all(255));
cv::line(img, cv::Point(0, 0), cv::Point(img.cols, img.rows), cv::Scalar::all(255));
cv::line(img, cv::Point(img.cols, 0), cv::Point(0, img.rows), cv::Scalar::all(255));
}
static void drawLines(cv::Mat& dst, const std::vector<cv::Vec2f>& lines)
{
dst.setTo(cv::Scalar::all(0));
for (size_t i = 0; i < lines.size(); ++i)
{
float rho = lines[i][0], theta = lines[i][1];
cv::Point pt1, pt2;
double a = std::cos(theta), b = std::sin(theta);
double x0 = a*rho, y0 = b*rho;
pt1.x = cvRound(x0 + 1000*(-b));
pt1.y = cvRound(y0 + 1000*(a));
pt2.x = cvRound(x0 - 1000*(-b));
pt2.y = cvRound(y0 - 1000*(a));
cv::line(dst, pt1, pt2, cv::Scalar::all(255));
}
}
};
TEST_P(HoughLines, Accuracy)
{
const cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
const cv::Size size = GET_PARAM(1);
const bool useRoi = GET_PARAM(2);
const float rho = 1.0f;
const float theta = 1.5f * CV_PI / 180.0f;
const int threshold = 100;
cv::Mat src(size, CV_8UC1);
generateLines(src);
cv::gpu::GpuMat d_lines;
cv::gpu::HoughLines(loadMat(src, useRoi), d_lines, rho, theta, threshold);
std::vector<cv::Vec2f> lines;
cv::gpu::HoughLinesDownload(d_lines, lines);
cv::Mat dst(size, CV_8UC1);
drawLines(dst, lines);
ASSERT_MAT_NEAR(src, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, HoughLines, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
WHOLE_SUBMAT));
///////////////////////////////////////////////////////////////////////////////////////////////////////
// HoughCircles
PARAM_TEST_CASE(HoughCircles, cv::gpu::DeviceInfo, cv::Size, UseRoi)
{
static void drawCircles(cv::Mat& dst, const std::vector<cv::Vec3f>& circles, bool fill)
{
dst.setTo(cv::Scalar::all(0));
for (size_t i = 0; i < circles.size(); ++i)
cv::circle(dst, cv::Point2f(circles[i][0], circles[i][1]), (int)circles[i][2], cv::Scalar::all(255), fill ? -1 : 1);
}
};
TEST_P(HoughCircles, Accuracy)
{
const cv::gpu::DeviceInfo devInfo = GET_PARAM(0);
cv::gpu::setDevice(devInfo.deviceID());
const cv::Size size = GET_PARAM(1);
const bool useRoi = GET_PARAM(2);
const float dp = 2.0f;
const float minDist = 10.0f;
const int minRadius = 10;
const int maxRadius = 20;
const int cannyThreshold = 100;
const int votesThreshold = 20;
std::vector<cv::Vec3f> circles_gold(4);
circles_gold[0] = cv::Vec3i(20, 20, minRadius);
circles_gold[1] = cv::Vec3i(90, 87, minRadius + 3);
circles_gold[2] = cv::Vec3i(30, 70, minRadius + 8);
circles_gold[3] = cv::Vec3i(80, 10, maxRadius);
cv::Mat src(size, CV_8UC1);
drawCircles(src, circles_gold, true);
cv::gpu::GpuMat d_circles;
cv::gpu::HoughCircles(loadMat(src, useRoi), d_circles, CV_HOUGH_GRADIENT, dp, minDist, cannyThreshold, votesThreshold, minRadius, maxRadius);
std::vector<cv::Vec3f> circles;
cv::gpu::HoughCirclesDownload(d_circles, circles);
ASSERT_FALSE(circles.empty());
for (size_t i = 0; i < circles.size(); ++i)
{
cv::Vec3f cur = circles[i];
bool found = false;
for (size_t j = 0; j < circles_gold.size(); ++j)
{
cv::Vec3f gold = circles_gold[j];
if (std::fabs(cur[0] - gold[0]) < minDist && std::fabs(cur[1] - gold[1]) < minDist && std::fabs(cur[2] - gold[2]) < minDist)
{
found = true;
break;
}
}
ASSERT_TRUE(found);
}
}
INSTANTIATE_TEST_CASE_P(GPU_ImgProc, HoughCircles, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
WHOLE_SUBMAT));
} // namespace
#endif // HAVE_CUDA